
"""simple docstring"""


import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
						import jax.numpy as jnp




class  __lowerCamelCase  ( lowerCAmelCase			):




				def __get__(				self							,						UpperCAmelCase							,						UpperCAmelCase=None   ):
							# See docs.python.org/3/howto/descriptor.html#properties
							if obj is None:
										return self
							if self.fget is None:
										raise AttributeError('''unreadable attribute'''   )
							lowerCamelCase_					=  '''__cached_''' + self.fget.__name__
							lowerCamelCase_					=  getattr(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
							if cached is None:
										lowerCamelCase_					=  self.fget(UpperCAmelCase   )
										setattr(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
							return cached






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  val.lower()
			if val in {"y", "yes", "t", "true", "on", "1"}:
						return 1
			if val in {"n", "no", "f", "false", "off", "0"}:
						return 0
			raise ValueError(f"invalid truth value {val!r}"						)






def  lowercase						(	lowerCAmelCase__						):
			if is_torch_fx_proxy(lowerCAmelCase__						):
						return True
			if is_torch_available():
						import torch

						if isinstance(lowerCAmelCase__	,torch.Tensor						):
									return True
			if is_tf_available():
						import tensorflow as tf

						if isinstance(lowerCAmelCase__	,tf.Tensor						):
									return True

			if is_flax_available():
						import jax.numpy as jnp
						from jax.core import Tracer

						if isinstance(lowerCAmelCase__	,(jnp.ndarray, Tracer)						):
									return True

			return isinstance(lowerCAmelCase__	,np.ndarray						)






def  lowercase						(	lowerCAmelCase__						):
			return isinstance(lowerCAmelCase__	,np.ndarray						)






def  lowercase						(	lowerCAmelCase__						):
			return _is_numpy(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			import torch

			return isinstance(lowerCAmelCase__	,torch.Tensor						)






def  lowercase						(	lowerCAmelCase__						):
			return False if not is_torch_available() else _is_torch(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			import torch

			return isinstance(lowerCAmelCase__	,torch.device						)






def  lowercase						(	lowerCAmelCase__						):
			return False if not is_torch_available() else _is_torch_device(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			import torch

			if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						if hasattr(lowerCAmelCase__	,lowerCAmelCase__						):
									lowerCamelCase_					=  getattr(lowerCAmelCase__	,lowerCAmelCase__						)
						else:
									return False
			return isinstance(lowerCAmelCase__	,torch.dtype						)






def  lowercase						(	lowerCAmelCase__						):
			return False if not is_torch_available() else _is_torch_dtype(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			import tensorflow as tf

			return isinstance(lowerCAmelCase__	,tf.Tensor						)






def  lowercase						(	lowerCAmelCase__						):
			return False if not is_tf_available() else _is_tensorflow(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			import tensorflow as tf

			# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
			if hasattr(lowerCAmelCase__	,'''is_symbolic_tensor'''						):
						return tf.is_symbolic_tensor(lowerCAmelCase__						)
			return type(lowerCAmelCase__						) == tf.Tensor






def  lowercase						(	lowerCAmelCase__						):
			return False if not is_tf_available() else _is_tf_symbolic_tensor(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			import jax.numpy as jnp  # noqa: F811

			return isinstance(lowerCAmelCase__	,jnp.ndarray						)






def  lowercase						(	lowerCAmelCase__						):
			return False if not is_flax_available() else _is_jax(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			if isinstance(lowerCAmelCase__	,(dict, UserDict)						):
						return {k: to_py_obj(lowerCAmelCase__						) for k, v in obj.items()}
			elif isinstance(lowerCAmelCase__	,(list, tuple)						):
						return [to_py_obj(lowerCAmelCase__						) for o in obj]
			elif is_tf_tensor(lowerCAmelCase__						):
						return obj.numpy().tolist()
			elif is_torch_tensor(lowerCAmelCase__						):
						return obj.detach().cpu().tolist()
			elif is_jax_tensor(lowerCAmelCase__						):
						return np.asarray(lowerCAmelCase__						).tolist()
			elif isinstance(lowerCAmelCase__	,(np.ndarray, np.number)						):  # tolist also works on 0d np arrays
						return obj.tolist()
			else:
						return obj






def  lowercase						(	lowerCAmelCase__						):
			if isinstance(lowerCAmelCase__	,(dict, UserDict)						):
						return {k: to_numpy(lowerCAmelCase__						) for k, v in obj.items()}
			elif isinstance(lowerCAmelCase__	,(list, tuple)						):
						return np.array(lowerCAmelCase__						)
			elif is_tf_tensor(lowerCAmelCase__						):
						return obj.numpy()
			elif is_torch_tensor(lowerCAmelCase__						):
						return obj.detach().cpu().numpy()
			elif is_jax_tensor(lowerCAmelCase__						):
						return np.asarray(lowerCAmelCase__						)
			else:
						return obj




class  __lowerCamelCase  ( lowerCAmelCase			):




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  fields(self   )

							# Safety and consistency checks
							if not len(UpperCAmelCase   ):
										raise ValueError(f"{self.__class__.__name__} has no fields."   )
							if not all(field.default is None for field in class_fields[1:]   ):
										raise ValueError(f"{self.__class__.__name__} should not have more than one required field."   )

							lowerCamelCase_					=  getattr(self							,						class_fields[0].name   )
							lowerCamelCase_					=  all(getattr(self							,						field.name   ) is None for field in class_fields[1:]   )

							if other_fields_are_none and not is_tensor(UpperCAmelCase   ):
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  first_field.items()
													lowerCamelCase_					=  True
										else:
													try:
																lowerCamelCase_					=  iter(UpperCAmelCase   )
																lowerCamelCase_					=  True
													except TypeError:
																lowerCamelCase_					=  False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
										if first_field_iterator:
													for idx, element in enumerate(UpperCAmelCase   ):
																if (
																    not isinstance(UpperCAmelCase							,						(list, tuple)   )
																    or not len(UpperCAmelCase   ) == 2
																    or not isinstance(element[0]							,						UpperCAmelCase   )
																):
																			if idx == 0:
																						# If we do not have an iterator of key/values, set it as attribute
																						lowerCamelCase_					=  first_field
																			else:
																						# If we have a mixed iterator, raise an error
																						raise ValueError(
																						    f"Cannot set key/value for {element}. It needs to be a tuple (key, value)."   )
																			break
																setattr(self							,						element[0]							,						element[1]   )
																if element[1] is not None:
																			lowerCamelCase_					=  element[1]
										elif first_field is not None:
													lowerCamelCase_					=  first_field
							else:
										for field in class_fields:
													lowerCamelCase_					=  getattr(self							,						field.name   )
													if v is not None:
																lowerCamelCase_					=  v




				def __delitem__(				self							,						*UpperCAmelCase							,						**UpperCAmelCase   ):
							raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."   )




				def        UpperCAmelCase__						(				self							,						*UpperCAmelCase							,						**UpperCAmelCase   ):
							raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."   )




				def        UpperCAmelCase__						(				self							,						*UpperCAmelCase							,						**UpperCAmelCase   ):
							raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance."   )




				def        UpperCAmelCase__						(				self							,						*UpperCAmelCase							,						**UpperCAmelCase   ):
							raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance."   )




				def __getitem__(				self							,						UpperCAmelCase   ):
							if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
										lowerCamelCase_					=  dict(self.items()   )
										return inner_dict[k]
							else:
										return self.to_tuple()[k]




				def __setattr__(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							if name in self.keys() and value is not None:
										# Don't call self.__setitem__ to avoid recursion errors
										super().__setitem__(UpperCAmelCase							,						UpperCAmelCase   )
							super().__setattr__(UpperCAmelCase							,						UpperCAmelCase   )




				def __setitem__(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							# Will raise a KeyException if needed
							super().__setitem__(UpperCAmelCase							,						UpperCAmelCase   )
							# Don't call self.__setattr__ to avoid recursion errors
							super().__setattr__(UpperCAmelCase							,						UpperCAmelCase   )





				def        UpperCAmelCase__						(				self   ):
							return tuple(self[k] for k in self.keys()   )




class  __lowerCamelCase  ( lowerCAmelCase						,	lowerCAmelCase			):




				@classmethod
				def        UpperCAmelCase__						(				cls							,						UpperCAmelCase   ):
							raise ValueError(
							    f"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()   )}"   )




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Tuple          =    'longest'
				a__:      Dict          =    'max_length'
				a__:      Optional[int]          =    'do_not_pad'




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Any          =    'pt'
				a__:      Union[str, Any]          =    'tf'
				a__:      int          =    'np'
				a__:      List[str]          =    'jax'




class  __lowerCamelCase  :




				def __init__(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  context_managers
							lowerCamelCase_					=  ExitStack()




				def __enter__(				self   ):
							for context_manager in self.context_managers:
										self.stack.enter_context(UpperCAmelCase   )





				def __exit__(				self							,						*UpperCAmelCase							,						**UpperCAmelCase   ):
							self.stack.__exit__(*UpperCAmelCase							,						**UpperCAmelCase   )






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  infer_framework(lowerCAmelCase__						)
			if framework == "tf":
						lowerCamelCase_					=  inspect.signature(model_class.call						)  # TensorFlow models
			elif framework == "pt":
						lowerCamelCase_					=  inspect.signature(model_class.forward						)  # PyTorch models
			else:
						lowerCamelCase_					=  inspect.signature(model_class.__call__						)  # Flax models

			for p in signature.parameters:
						if p == "return_loss" and signature.parameters[p].default is True:
									return True

			return False






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  model_class.__name__
			lowerCamelCase_					=  infer_framework(lowerCAmelCase__						)
			if framework == "tf":
						lowerCamelCase_					=  inspect.signature(model_class.call						)  # TensorFlow models
			elif framework == "pt":
						lowerCamelCase_					=  inspect.signature(model_class.forward						)  # PyTorch models
			else:
						lowerCamelCase_					=  inspect.signature(model_class.__call__						)  # Flax models

			if "QuestionAnswering" in model_name:
						return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
			else:
						return [p for p in signature.parameters if "label" in p]






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__ = ""	,lowerCAmelCase__ = "."						):

			def _flatten_dict(lowerCAmelCase__	,lowerCAmelCase__=""	,lowerCAmelCase__="."						):
						for k, v in d.items():
									lowerCamelCase_					=  str(lowerCAmelCase__						) + delimiter + str(lowerCAmelCase__						) if parent_key else k
									if v and isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
												yield from flatten_dict(lowerCAmelCase__	,lowerCAmelCase__	,delimiter=lowerCAmelCase__						).items()
									else:
												yield key, v

			return dict(_flatten_dict(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)						)






@contextmanager
def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__ = False						):
			if use_temp_dir:
						with tempfile.TemporaryDirectory() as tmp_dir:
									yield tmp_dir
			else:
						yield working_dir






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=None						):
			if is_numpy_array(lowerCAmelCase__						):
						return np.transpose(lowerCAmelCase__	,axes=lowerCAmelCase__						)
			elif is_torch_tensor(lowerCAmelCase__						):
						return array.T if axes is None else array.permute(*lowerCAmelCase__						)
			elif is_tf_tensor(lowerCAmelCase__						):
						import tensorflow as tf

						return tf.transpose(lowerCAmelCase__	,perm=lowerCAmelCase__						)
			elif is_jax_tensor(lowerCAmelCase__						):
						return jnp.transpose(lowerCAmelCase__	,axes=lowerCAmelCase__						)
			else:
						raise ValueError(f"Type not supported for transpose: {type(lowerCAmelCase__						)}."						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			if is_numpy_array(lowerCAmelCase__						):
						return np.reshape(lowerCAmelCase__	,lowerCAmelCase__						)
			elif is_torch_tensor(lowerCAmelCase__						):
						return array.reshape(*lowerCAmelCase__						)
			elif is_tf_tensor(lowerCAmelCase__						):
						import tensorflow as tf

						return tf.reshape(lowerCAmelCase__	,lowerCAmelCase__						)
			elif is_jax_tensor(lowerCAmelCase__						):
						return jnp.reshape(lowerCAmelCase__	,lowerCAmelCase__						)
			else:
						raise ValueError(f"Type not supported for reshape: {type(lowerCAmelCase__						)}."						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=None						):
			if is_numpy_array(lowerCAmelCase__						):
						return np.squeeze(lowerCAmelCase__	,axis=lowerCAmelCase__						)
			elif is_torch_tensor(lowerCAmelCase__						):
						return array.squeeze() if axis is None else array.squeeze(dim=lowerCAmelCase__						)
			elif is_tf_tensor(lowerCAmelCase__						):
						import tensorflow as tf

						return tf.squeeze(lowerCAmelCase__	,axis=lowerCAmelCase__						)
			elif is_jax_tensor(lowerCAmelCase__						):
						return jnp.squeeze(lowerCAmelCase__	,axis=lowerCAmelCase__						)
			else:
						raise ValueError(f"Type not supported for squeeze: {type(lowerCAmelCase__						)}."						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			if is_numpy_array(lowerCAmelCase__						):
						return np.expand_dims(lowerCAmelCase__	,lowerCAmelCase__						)
			elif is_torch_tensor(lowerCAmelCase__						):
						return array.unsqueeze(dim=lowerCAmelCase__						)
			elif is_tf_tensor(lowerCAmelCase__						):
						import tensorflow as tf

						return tf.expand_dims(lowerCAmelCase__	,axis=lowerCAmelCase__						)
			elif is_jax_tensor(lowerCAmelCase__						):
						return jnp.expand_dims(lowerCAmelCase__	,axis=lowerCAmelCase__						)
			else:
						raise ValueError(f"Type not supported for expand_dims: {type(lowerCAmelCase__						)}."						)






def  lowercase						(	lowerCAmelCase__						):
			if is_numpy_array(lowerCAmelCase__						):
						return np.size(lowerCAmelCase__						)
			elif is_torch_tensor(lowerCAmelCase__						):
						return array.numel()
			elif is_tf_tensor(lowerCAmelCase__						):
						import tensorflow as tf

						return tf.size(lowerCAmelCase__						)
			elif is_jax_tensor(lowerCAmelCase__						):
						return array.size
			else:
						raise ValueError(f"Type not supported for expand_dims: {type(lowerCAmelCase__						)}."						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			for key, value in auto_map.items():
						if isinstance(lowerCAmelCase__	,(tuple, list)						):
									lowerCamelCase_					=  [f"{repo_id}--{v}" if (v is not None and '''--''' not in v) else v for v in value]
						elif value is not None and "--" not in value:
									lowerCamelCase_					=  f"{repo_id}--{value}"

			return auto_map






def  lowercase						(	lowerCAmelCase__						):
			for base_class in inspect.getmro(lowerCAmelCase__						):
						lowerCamelCase_					=  base_class.__module__
						lowerCamelCase_					=  base_class.__name__
						if module.startswith('''tensorflow'''						) or module.startswith('''keras'''						) or name == "TFPreTrainedModel":
									return "tf"
						elif module.startswith('''torch'''						) or name == "PreTrainedModel":
									return "pt"
						elif module.startswith('''flax'''						) or module.startswith('''jax'''						) or name == "FlaxPreTrainedModel":
									return "flax"
			else:
						raise TypeError(f"Could not infer framework from class {model_class}."						)











"""simple docstring"""





import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTMSNForImageClassification, ViTMSNModel
  from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class 	a		:






    def __init__(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				str=13			,		__SCREAMING_SNAKE_CASE	:				int=30			,		__SCREAMING_SNAKE_CASE	:				List[Any]=2			,		__SCREAMING_SNAKE_CASE	:				Dict=3			,		__SCREAMING_SNAKE_CASE	:				List[str]=True			,		__SCREAMING_SNAKE_CASE	:				str=True			,		__SCREAMING_SNAKE_CASE	:				Any=32			,		__SCREAMING_SNAKE_CASE	:				List[str]=5			,		__SCREAMING_SNAKE_CASE	:				List[str]=4			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=37			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]="gelu"			,		__SCREAMING_SNAKE_CASE	:				List[Any]=0.1			,		__SCREAMING_SNAKE_CASE	:				Tuple=0.1			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=10			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=0.02			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=None			,		)	->						List[str]:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		image_size
      lowerCamelCase_       	=		patch_size
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		is_training
      lowerCamelCase_       	=		use_labels
      lowerCamelCase_       	=		hidden_size
      lowerCamelCase_       	=		num_hidden_layers
      lowerCamelCase_       	=		num_attention_heads
      lowerCamelCase_       	=		intermediate_size
      lowerCamelCase_       	=		hidden_act
      lowerCamelCase_       	=		hidden_dropout_prob
      lowerCamelCase_       	=		attention_probs_dropout_prob
      lowerCamelCase_       	=		type_sequence_label_size
      lowerCamelCase_       	=		initializer_range
      lowerCamelCase_       	=		scope

      # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      lowerCamelCase_       	=		(image_size // patch_size) ** 2
      lowerCamelCase_       	=		num_patches + 1






    def   UpperCamelCase			(			self	:				Dict   )	->						Union[str, Any]:
      lowerCamelCase_       	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      lowerCamelCase_       	=		None
      if self.use_labels:
        lowerCamelCase_       	=		ids_tensor([self.batch_size]			,		self.type_sequence_label_size   )

      lowerCamelCase_       	=		self.get_config()

      return config, pixel_values, labels






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Union[str, Any]:
      return ViTMSNConfig(
          image_size=self.image_size			,		patch_size=self.patch_size			,		num_channels=self.num_channels			,		hidden_size=self.hidden_size			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		hidden_act=self.hidden_act			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		initializer_range=self.initializer_range			,		)






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Any:
      lowerCamelCase_       	=		ViTMSNModel(config=__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size)   )






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				str   )	->						List[str]:
      lowerCamelCase_       	=		self.type_sequence_label_size
      lowerCamelCase_       	=		ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		labels=__SCREAMING_SNAKE_CASE   )
      print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'   )
      print('Labels: {labels}'   )
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.type_sequence_label_size)   )

      # test greyscale images
      lowerCamelCase_       	=		1
      lowerCamelCase_       	=		ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()

      lowerCamelCase_       	=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.type_sequence_label_size)   )






    def   UpperCamelCase			(			self	:				List[str]   )	->						Tuple:
      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		config_and_inputs
      lowerCamelCase_       	=		{'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class 	a		(   __snake_case    , __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	str		=					(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE		:	Dict		=					(
        {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE		:	Dict		=					False
    SCREAMING_SNAKE_CASE		:	Union[str, Any]		=					False
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					False
    SCREAMING_SNAKE_CASE		:	str		=					False






    def   UpperCamelCase			(			self	:				List[Any]   )	->						List[str]:
      lowerCamelCase_       	=		ViTMSNModelTester(self   )
      lowerCamelCase_       	=		ConfigTester(self			,		config_class=__SCREAMING_SNAKE_CASE			,		has_text_modality=__SCREAMING_SNAKE_CASE			,		hidden_size=37   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      self.config_tester.run_common_tests()






    @unittest.skip(reason='ViTMSN does not use inputs_embeds'   )
    def   UpperCamelCase			(			self	:				Tuple   )	->						Union[str, Any]:
      pass






    def   UpperCamelCase			(			self	:				List[Any]   )	->						List[Any]:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        self.assertIsInstance(model.get_input_embeddings()			,		(nn.Module)   )
        lowerCamelCase_       	=		model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE			,		nn.Linear   )   )






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Any:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		inspect.signature(model.forward   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCamelCase_       	=		[*signature.parameters.keys()]

        lowerCamelCase_       	=		['pixel_values']
        self.assertListEqual(arg_names[:1]			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[int]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase_       	=		ViTMSNModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
        self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )







def 	lowerCamelCase__			(				) ->							List[Any]:
  lowerCamelCase_       	=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
  return image






@require_torch
@require_vision
class 	a		(   unittest.TestCase						):






    @cached_property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Any:
      return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'   ) if is_vision_available() else None






    @slow
    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      torch.manual_seed(2   )
      lowerCamelCase_       	=		ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'   ).to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.default_image_processor
      lowerCamelCase_       	=		prepare_img()
      lowerCamelCase_       	=		image_processor(images=__SCREAMING_SNAKE_CASE			,		return_tensors='pt'   ).to(__SCREAMING_SNAKE_CASE   )

      # forward pass
      with torch.no_grad():
        lowerCamelCase_       	=		model(**__SCREAMING_SNAKE_CASE   )

      # verify the logits
      lowerCamelCase_       	=		torch.Size((1, 1000)   )
      self.assertEqual(outputs.logits.shape			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.tensor([-0.0_803, -0.4_454, -0.2_375]   ).to(__SCREAMING_SNAKE_CASE   )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]			,		__SCREAMING_SNAKE_CASE			,		atol=1e-4   )   )




